Overview

Dataset statistics

Number of variables31
Number of observations5000
Missing cells4273
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory248.0 B

Variable types

Numeric16
Categorical13
Boolean2

Alerts

Supervisor ID has constant value "2140092"Constant
Emp Status has constant value "True"Constant
Emp Type has constant value "Fulltime"Constant
Required Hours has constant value "1920"Constant
Actual Hours has constant value "1920"Constant
Name has a high cardinality: 208 distinct valuesHigh cardinality
Hire Date has a high cardinality: 170 distinct valuesHigh cardinality
Datte of Birth has a high cardinality: 223 distinct valuesHigh cardinality
Level is highly overall correlated with Salary and 5 other fieldsHigh correlation
Salary is highly overall correlated with Level and 5 other fieldsHigh correlation
Salary Min is highly overall correlated with Level and 5 other fieldsHigh correlation
Salary Max is highly overall correlated with Level and 5 other fieldsHigh correlation
Pre-increment salary is highly overall correlated with Level and 5 other fieldsHigh correlation
Basic is highly overall correlated with Level and 5 other fieldsHigh correlation
Housing is highly overall correlated with Level and 5 other fieldsHigh correlation
Test is highly overall correlated with Promotion EligibilityHigh correlation
Promotion Eligibility is highly overall correlated with TestHigh correlation
Housing has 4273 (85.5%) missing valuesMissing
Test is uniformly distributedUniform
Employee ID has unique valuesUnique
Unpaid Leave has 236 (4.7%) zerosZeros

Reproduction

Analysis started2023-06-28 05:08:06.683648
Analysis finished2023-06-28 05:08:30.871315
Duration24.19 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Employee ID
Real number (ℝ)

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8817461.1
Minimum306031
Maximum9144832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:30.954603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum306031
5-th percentile9140082.9
Q19141082.8
median9142332.5
Q39143582.2
95-th percentile9144582.1
Maximum9144832
Range8838801
Interquartile range (IQR)2499.5

Descriptive statistics

Standard deviation1487720.8
Coefficient of variation (CV)0.1687244
Kurtosis17.322025
Mean8817461.1
Median Absolute Deviation (MAD)1250
Skewness-4.3822489
Sum4.4087306 × 1010
Variance2.2133132 × 1012
MonotonicityNot monotonic
2023-06-28T10:38:31.034255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2140092 1
 
< 0.1%
9143163 1
 
< 0.1%
9143170 1
 
< 0.1%
9143169 1
 
< 0.1%
9143168 1
 
< 0.1%
9143167 1
 
< 0.1%
9143166 1
 
< 0.1%
9143165 1
 
< 0.1%
9143164 1
 
< 0.1%
9143162 1
 
< 0.1%
Other values (4990) 4990
99.8%
ValueCountFrequency (%)
306031 1
< 0.1%
312031 1
< 0.1%
313133 1
< 0.1%
314051 1
< 0.1%
314083 1
< 0.1%
316041 1
< 0.1%
316091 1
< 0.1%
508071 1
< 0.1%
1000002 1
< 0.1%
1020070 1
< 0.1%
ValueCountFrequency (%)
9144832 1
< 0.1%
9144831 1
< 0.1%
9144830 1
< 0.1%
9144829 1
< 0.1%
9144828 1
< 0.1%
9144827 1
< 0.1%
9144826 1
< 0.1%
9144825 1
< 0.1%
9144824 1
< 0.1%
9144823 1
< 0.1%

Name
Categorical

Distinct208
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Mohammed Last Name
 
108
Mohamed Last Name
 
107
Abdul Last Name
 
65
Mohammad Last Name
 
44
Arun Last Name
 
44
Other values (203)
4632 

Length

Max length22
Median length20
Mean length16.095
Min length11

Characters and Unicode

Total characters80475
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnfas Last Name
2nd rowLennard Last Name
3rd rowEdgardo Last Name
4th rowChanna Last Name
5th rowLinda Last Name

Common Values

ValueCountFrequency (%)
Mohammed Last Name 108
 
2.2%
Mohamed Last Name 107
 
2.1%
Abdul Last Name 65
 
1.3%
Mohammad Last Name 44
 
0.9%
Arun Last Name 44
 
0.9%
Francis Last Name 44
 
0.9%
Shameer Last Name 44
 
0.9%
Anil Last Name 44
 
0.9%
Muhammed Last Name 44
 
0.9%
Christine Last Name 43
 
0.9%
Other values (198) 4413
88.3%

Length

2023-06-28T10:38:31.132148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
name 5000
33.3%
last 5000
33.3%
mohammed 108
 
0.7%
mohamed 107
 
0.7%
abdul 65
 
0.4%
mohammad 44
 
0.3%
arun 44
 
0.3%
francis 44
 
0.3%
shameer 44
 
0.3%
anil 44
 
0.3%
Other values (199) 4500
30.0%

Most occurring characters

ValueCountFrequency (%)
a 14208
17.7%
10000
12.4%
e 7792
9.7%
m 6516
8.1%
s 5955
 
7.4%
t 5803
 
7.2%
N 5171
 
6.4%
L 5065
 
6.3%
h 2013
 
2.5%
i 1994
 
2.5%
Other values (42) 15958
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55160
68.5%
Uppercase Letter 15293
 
19.0%
Space Separator 10000
 
12.4%
Other Punctuation 22
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14208
25.8%
e 7792
14.1%
m 6516
11.8%
s 5955
10.8%
t 5803
10.5%
h 2013
 
3.6%
i 1994
 
3.6%
n 1779
 
3.2%
r 1625
 
2.9%
d 1415
 
2.6%
Other values (16) 6060
11.0%
Uppercase Letter
ValueCountFrequency (%)
N 5171
33.8%
L 5065
33.1%
A 869
 
5.7%
M 819
 
5.4%
S 734
 
4.8%
R 389
 
2.5%
J 282
 
1.8%
I 215
 
1.4%
H 194
 
1.3%
B 193
 
1.3%
Other values (14) 1362
 
8.9%
Space Separator
ValueCountFrequency (%)
10000
100.0%
Other Punctuation
ValueCountFrequency (%)
. 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70453
87.5%
Common 10022
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14208
20.2%
e 7792
11.1%
m 6516
9.2%
s 5955
8.5%
t 5803
8.2%
N 5171
 
7.3%
L 5065
 
7.2%
h 2013
 
2.9%
i 1994
 
2.8%
n 1779
 
2.5%
Other values (40) 14157
20.1%
Common
ValueCountFrequency (%)
10000
99.8%
. 22
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14208
17.7%
10000
12.4%
e 7792
9.7%
m 6516
8.1%
s 5955
 
7.4%
t 5803
 
7.2%
N 5171
 
6.4%
L 5065
 
6.3%
h 2013
 
2.5%
i 1994
 
2.5%
Other values (42) 15958
19.8%

Title
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Officer
1948 
Executive
1927 
Manger
282 
Assistant Manager
281 
Senior Manager
281 

Length

Max length17
Median length15
Mean length9.1194
Min length6

Characters and Unicode

Total characters45597
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManger
2nd rowOfficer
3rd rowExecutive
4th rowAssistant Manager
5th rowSenior Manager

Common Values

ValueCountFrequency (%)
Officer 1948
39.0%
Executive 1927
38.5%
Manger 282
 
5.6%
Assistant Manager 281
 
5.6%
Senior Manager 281
 
5.6%
Finance Manager 281
 
5.6%

Length

2023-06-28T10:38:31.206807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T10:38:31.301126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
officer 1948
33.3%
executive 1927
33.0%
manager 843
14.4%
manger 282
 
4.8%
assistant 281
 
4.8%
senior 281
 
4.8%
finance 281
 
4.8%

Most occurring characters

ValueCountFrequency (%)
e 7489
16.4%
i 4718
10.3%
c 4156
 
9.1%
f 3896
 
8.5%
r 3354
 
7.4%
a 2530
 
5.5%
t 2489
 
5.5%
n 2249
 
4.9%
O 1948
 
4.3%
v 1927
 
4.2%
Other values (11) 10841
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38911
85.3%
Uppercase Letter 5843
 
12.8%
Space Separator 843
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7489
19.2%
i 4718
12.1%
c 4156
10.7%
f 3896
10.0%
r 3354
8.6%
a 2530
 
6.5%
t 2489
 
6.4%
n 2249
 
5.8%
v 1927
 
5.0%
u 1927
 
5.0%
Other values (4) 4176
10.7%
Uppercase Letter
ValueCountFrequency (%)
O 1948
33.3%
E 1927
33.0%
M 1125
19.3%
A 281
 
4.8%
S 281
 
4.8%
F 281
 
4.8%
Space Separator
ValueCountFrequency (%)
843
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44754
98.2%
Common 843
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7489
16.7%
i 4718
10.5%
c 4156
9.3%
f 3896
 
8.7%
r 3354
 
7.5%
a 2530
 
5.7%
t 2489
 
5.6%
n 2249
 
5.0%
O 1948
 
4.4%
v 1927
 
4.3%
Other values (10) 9998
22.3%
Common
ValueCountFrequency (%)
843
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7489
16.4%
i 4718
10.3%
c 4156
 
9.1%
f 3896
 
8.5%
r 3354
 
7.4%
a 2530
 
5.5%
t 2489
 
5.5%
n 2249
 
4.9%
O 1948
 
4.3%
v 1927
 
4.2%
Other values (11) 10841
23.8%

Level
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4912
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:31.364207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6960241
Coefficient of variation (CV)0.68080609
Kurtosis0.80129733
Mean2.4912
Median Absolute Deviation (MAD)1
Skewness1.1510849
Sum12456
Variance2.8764979
MonotonicityNot monotonic
2023-06-28T10:38:31.444660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 1987
39.7%
2 1066
21.3%
3 709
 
14.2%
4 521
 
10.4%
5 350
 
7.0%
6 280
 
5.6%
8 43
 
0.9%
9 22
 
0.4%
7 22
 
0.4%
ValueCountFrequency (%)
1 1987
39.7%
2 1066
21.3%
3 709
 
14.2%
4 521
 
10.4%
5 350
 
7.0%
6 280
 
5.6%
7 22
 
0.4%
8 43
 
0.9%
9 22
 
0.4%
ValueCountFrequency (%)
9 22
 
0.4%
8 43
 
0.9%
7 22
 
0.4%
6 280
 
5.6%
5 350
 
7.0%
4 521
 
10.4%
3 709
 
14.2%
2 1066
21.3%
1 1987
39.7%

Department
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
F&B
1832 
Shared Services
1378 
Technical
646 
Legal
572 
Civil
484 

Length

Max length15
Median length10
Mean length7.628
Min length3

Characters and Unicode

Total characters38140
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManagement
2nd rowShared Services
3rd rowShared Services
4th rowManagement
5th rowManagement

Common Values

ValueCountFrequency (%)
F&B 1832
36.6%
Shared Services 1378
27.6%
Technical 646
 
12.9%
Legal 572
 
11.4%
Civil 484
 
9.7%
Management 88
 
1.8%

Length

2023-06-28T10:38:31.520836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T10:38:31.615126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
f&b 1832
28.7%
shared 1378
21.6%
services 1378
21.6%
technical 646
 
10.1%
legal 572
 
9.0%
civil 484
 
7.6%
management 88
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 5528
14.5%
i 2992
 
7.8%
a 2772
 
7.3%
S 2756
 
7.2%
r 2756
 
7.2%
c 2670
 
7.0%
h 2024
 
5.3%
v 1862
 
4.9%
& 1832
 
4.8%
F 1832
 
4.8%
Other values (13) 11116
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26720
70.1%
Uppercase Letter 8210
 
21.5%
Other Punctuation 1832
 
4.8%
Space Separator 1378
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5528
20.7%
i 2992
11.2%
a 2772
10.4%
r 2756
10.3%
c 2670
10.0%
h 2024
 
7.6%
v 1862
 
7.0%
l 1702
 
6.4%
d 1378
 
5.2%
s 1378
 
5.2%
Other values (4) 1658
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
S 2756
33.6%
F 1832
22.3%
B 1832
22.3%
T 646
 
7.9%
L 572
 
7.0%
C 484
 
5.9%
M 88
 
1.1%
Other Punctuation
ValueCountFrequency (%)
& 1832
100.0%
Space Separator
ValueCountFrequency (%)
1378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34930
91.6%
Common 3210
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5528
15.8%
i 2992
 
8.6%
a 2772
 
7.9%
S 2756
 
7.9%
r 2756
 
7.9%
c 2670
 
7.6%
h 2024
 
5.8%
v 1862
 
5.3%
F 1832
 
5.2%
B 1832
 
5.2%
Other values (11) 7906
22.6%
Common
ValueCountFrequency (%)
& 1832
57.1%
1378
42.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5528
14.5%
i 2992
 
7.8%
a 2772
 
7.3%
S 2756
 
7.2%
r 2756
 
7.2%
c 2670
 
7.0%
h 2024
 
5.3%
v 1862
 
4.9%
& 1832
 
4.8%
F 1832
 
4.8%
Other values (13) 11116
29.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Female
2536 
Male
2464 

Length

Max length6
Median length6
Mean length5.0144
Min length4

Characters and Unicode

Total characters25072
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 2536
50.7%
Male 2464
49.3%

Length

2023-06-28T10:38:31.709569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T10:38:31.788237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 2536
50.7%
male 2464
49.3%

Most occurring characters

ValueCountFrequency (%)
e 7536
30.1%
a 5000
19.9%
l 5000
19.9%
F 2536
 
10.1%
m 2536
 
10.1%
M 2464
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20072
80.1%
Uppercase Letter 5000
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7536
37.5%
a 5000
24.9%
l 5000
24.9%
m 2536
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F 2536
50.7%
M 2464
49.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 25072
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7536
30.1%
a 5000
19.9%
l 5000
19.9%
F 2536
 
10.1%
m 2536
 
10.1%
M 2464
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7536
30.1%
a 5000
19.9%
l 5000
19.9%
F 2536
 
10.1%
m 2536
 
10.1%
M 2464
 
9.8%

Hire Date
Categorical

Distinct170
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
31-07-2016
 
212
16-07-2016
 
128
10-08-2016
 
126
23-07-2016
 
105
04-05-2016
 
88
Other values (165)
4341 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters50000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04-01-2015
2nd row22-03-2015
3rd row01-06-2016
4th row15-05-2016
5th row01-09-2008

Common Values

ValueCountFrequency (%)
31-07-2016 212
 
4.2%
16-07-2016 128
 
2.6%
10-08-2016 126
 
2.5%
23-07-2016 105
 
2.1%
04-05-2016 88
 
1.8%
15-01-2013 88
 
1.8%
21-01-2013 88
 
1.8%
10-09-2016 84
 
1.7%
11-02-2013 66
 
1.3%
12-06-2016 66
 
1.3%
Other values (160) 3949
79.0%

Length

2023-06-28T10:38:31.851388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
31-07-2016 212
 
4.2%
16-07-2016 128
 
2.6%
10-08-2016 126
 
2.5%
23-07-2016 105
 
2.1%
04-05-2016 88
 
1.8%
15-01-2013 88
 
1.8%
21-01-2013 88
 
1.8%
10-09-2016 84
 
1.7%
26-06-2016 66
 
1.3%
11-02-2013 66
 
1.3%
Other values (160) 3949
79.0%

Most occurring characters

ValueCountFrequency (%)
0 12249
24.5%
- 10000
20.0%
1 8851
17.7%
2 7590
15.2%
6 3247
 
6.5%
7 1653
 
3.3%
8 1577
 
3.2%
3 1565
 
3.1%
5 1291
 
2.6%
4 1247
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40000
80.0%
Dash Punctuation 10000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12249
30.6%
1 8851
22.1%
2 7590
19.0%
6 3247
 
8.1%
7 1653
 
4.1%
8 1577
 
3.9%
3 1565
 
3.9%
5 1291
 
3.2%
4 1247
 
3.1%
9 730
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12249
24.5%
- 10000
20.0%
1 8851
17.7%
2 7590
15.2%
6 3247
 
6.5%
7 1653
 
3.3%
8 1577
 
3.2%
3 1565
 
3.1%
5 1291
 
2.6%
4 1247
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12249
24.5%
- 10000
20.0%
1 8851
17.7%
2 7590
15.2%
6 3247
 
6.5%
7 1653
 
3.3%
8 1577
 
3.2%
3 1565
 
3.1%
5 1291
 
2.6%
4 1247
 
2.5%

Tenure
Real number (ℝ)

Distinct152
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.039628
Minimum5.08
Maximum22.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:31.945868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.08
5-th percentile5.43
Q16.83
median7.13
Q310.28
95-th percentile17.46
Maximum22.61
Range17.53
Interquartile range (IQR)3.45

Descriptive statistics

Standard deviation3.7395135
Coefficient of variation (CV)0.41368002
Kurtosis1.9049794
Mean9.039628
Median Absolute Deviation (MAD)0.73
Skewness1.6448428
Sum45198.14
Variance13.983961
MonotonicityNot monotonic
2023-06-28T10:38:32.041023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.86 254
 
5.1%
6.9 170
 
3.4%
6.83 169
 
3.4%
6.88 126
 
2.5%
6.84 106
 
2.1%
7.1 88
 
1.8%
10.39 88
 
1.8%
10.4 88
 
1.8%
6.75 84
 
1.7%
7.53 66
 
1.3%
Other values (142) 3761
75.2%
ValueCountFrequency (%)
5.08 22
0.4%
5.11 22
0.4%
5.15 22
0.4%
5.25 22
0.4%
5.31 22
0.4%
5.32 22
0.4%
5.33 22
0.4%
5.34 22
0.4%
5.4 22
0.4%
5.41 44
0.9%
ValueCountFrequency (%)
22.61 21
0.4%
22.16 22
0.4%
21.82 21
0.4%
20.73 21
0.4%
18.53 22
0.4%
18.45 22
0.4%
18.36 21
0.4%
18.34 22
0.4%
18.1 22
0.4%
17.92 22
0.4%

Supervisor ID
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
2140092
5000 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters35000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2140092
2nd row2140092
3rd row2140092
4th row2140092
5th row2140092

Common Values

ValueCountFrequency (%)
2140092 5000
100.0%

Length

2023-06-28T10:38:32.119574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T10:38:32.319525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2140092 5000
100.0%

Most occurring characters

ValueCountFrequency (%)
2 10000
28.6%
0 10000
28.6%
1 5000
14.3%
4 5000
14.3%
9 5000
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10000
28.6%
0 10000
28.6%
1 5000
14.3%
4 5000
14.3%
9 5000
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 35000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10000
28.6%
0 10000
28.6%
1 5000
14.3%
4 5000
14.3%
9 5000
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10000
28.6%
0 10000
28.6%
1 5000
14.3%
4 5000
14.3%
9 5000
14.3%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
1
1020 
3
1007 
2
999 
5
996 
4
978 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row2
4th row1
5th row4

Common Values

ValueCountFrequency (%)
1 1020
20.4%
3 1007
20.1%
2 999
20.0%
5 996
19.9%
4 978
19.6%

Length

2023-06-28T10:38:32.392439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T10:38:32.466917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1020
20.4%
3 1007
20.1%
2 999
20.0%
5 996
19.9%
4 978
19.6%

Most occurring characters

ValueCountFrequency (%)
1 1020
20.4%
3 1007
20.1%
2 999
20.0%
5 996
19.9%
4 978
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1020
20.4%
3 1007
20.1%
2 999
20.0%
5 996
19.9%
4 978
19.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1020
20.4%
3 1007
20.1%
2 999
20.0%
5 996
19.9%
4 978
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1020
20.4%
3 1007
20.1%
2 999
20.0%
5 996
19.9%
4 978
19.6%

Salary
Real number (ℝ)

Distinct78
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24803.653
Minimum4251
Maximum359183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:32.561790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4251
5-th percentile4783
Q15668
median9742
Q321254
95-th percentile95641
Maximum359183
Range354932
Interquartile range (IQR)15586

Descriptive statistics

Standard deviation42165.562
Coefficient of variation (CV)1.6999739
Kurtosis23.225532
Mean24803.653
Median Absolute Deviation (MAD)4428
Skewness4.3069751
Sum1.2401826 × 108
Variance1.7779346 × 109
MonotonicityNot monotonic
2023-06-28T10:38:32.656518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5314 596
 
11.9%
10627 284
 
5.7%
5668 242
 
4.8%
7971 190
 
3.8%
4605 176
 
3.5%
4783 171
 
3.4%
9565 169
 
3.4%
17712 149
 
3.0%
9742 132
 
2.6%
8856 109
 
2.2%
Other values (68) 2782
55.6%
ValueCountFrequency (%)
4251 44
 
0.9%
4605 176
 
3.5%
4783 171
 
3.4%
5066 44
 
0.9%
5314 596
11.9%
5580 43
 
0.9%
5668 242
4.8%
5845 43
 
0.9%
6040 44
 
0.9%
6377 66
 
1.3%
ValueCountFrequency (%)
359183 21
0.4%
247957 22
0.4%
230246 16
0.3%
212535 21
0.4%
196350 6
 
0.1%
196003 22
0.4%
159755 22
0.4%
127521 21
0.4%
123979 22
0.4%
118665 22
0.4%

Salary Min
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20796.95
Minimum5950
Maximum232900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:32.735707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5950
5-th percentile5950
Q15950
median9350
Q316150
95-th percentile85000
Maximum232900
Range226950
Interquartile range (IQR)10200

Descriptive statistics

Standard deviation31050.054
Coefficient of variation (CV)1.49301
Kurtosis21.956544
Mean20796.95
Median Absolute Deviation (MAD)3400
Skewness4.1904982
Sum1.0398475 × 108
Variance9.6410587 × 108
MonotonicityNot monotonic
2023-06-28T10:38:32.813978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5950 1987
39.7%
9350 1066
21.3%
16150 709
 
14.2%
28050 521
 
10.4%
43350 350
 
7.0%
85000 280
 
5.6%
232900 43
 
0.9%
196350 22
 
0.4%
128350 22
 
0.4%
ValueCountFrequency (%)
5950 1987
39.7%
9350 1066
21.3%
16150 709
 
14.2%
28050 521
 
10.4%
43350 350
 
7.0%
85000 280
 
5.6%
128350 22
 
0.4%
196350 22
 
0.4%
232900 43
 
0.9%
ValueCountFrequency (%)
232900 43
 
0.9%
196350 22
 
0.4%
128350 22
 
0.4%
85000 280
 
5.6%
43350 350
 
7.0%
28050 521
 
10.4%
16150 709
 
14.2%
9350 1066
21.3%
5950 1987
39.7%

Salary Max
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28137.05
Minimum8050
Maximum315100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:32.876995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8050
5-th percentile8050
Q18050
median12650
Q321850
95-th percentile115000
Maximum315100
Range307050
Interquartile range (IQR)13800

Descriptive statistics

Standard deviation42008.897
Coefficient of variation (CV)1.49301
Kurtosis21.956544
Mean28137.05
Median Absolute Deviation (MAD)4600
Skewness4.1904982
Sum1.4068525 × 108
Variance1.7647474 × 109
MonotonicityNot monotonic
2023-06-28T10:38:32.940043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8050 1987
39.7%
12650 1066
21.3%
21850 709
 
14.2%
37950 521
 
10.4%
58650 350
 
7.0%
115000 280
 
5.6%
315100 43
 
0.9%
265650 22
 
0.4%
173650 22
 
0.4%
ValueCountFrequency (%)
8050 1987
39.7%
12650 1066
21.3%
21850 709
 
14.2%
37950 521
 
10.4%
58650 350
 
7.0%
115000 280
 
5.6%
173650 22
 
0.4%
265650 22
 
0.4%
315100 43
 
0.9%
ValueCountFrequency (%)
315100 43
 
0.9%
265650 22
 
0.4%
173650 22
 
0.4%
115000 280
 
5.6%
58650 350
 
7.0%
37950 521
 
10.4%
21850 709
 
14.2%
12650 1066
21.3%
8050 1987
39.7%

Datte of Birth
Categorical

Distinct223
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
08-08-1987
 
64
17-01-1993
 
44
01-05-1989
 
44
14-01-1976
 
43
23-07-1988
 
43
Other values (218)
4762 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters50000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11-01-1987
2nd row30-03-1980
3rd row08-06-1991
4th row21-05-1992
5th row09-09-1978

Common Values

ValueCountFrequency (%)
08-08-1987 64
 
1.3%
17-01-1993 44
 
0.9%
01-05-1989 44
 
0.9%
14-01-1976 43
 
0.9%
23-07-1988 43
 
0.9%
27-07-1976 42
 
0.8%
22-07-1992 42
 
0.8%
10-05-1984 22
 
0.4%
15-03-1987 22
 
0.4%
19-03-1970 22
 
0.4%
Other values (213) 4612
92.2%

Length

2023-06-28T10:38:33.018872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
08-08-1987 64
 
1.3%
01-05-1989 44
 
0.9%
17-01-1993 44
 
0.9%
14-01-1976 43
 
0.9%
23-07-1988 43
 
0.9%
27-07-1976 42
 
0.8%
22-07-1992 42
 
0.8%
14-07-1971 22
 
0.4%
09-03-1976 22
 
0.4%
04-07-1993 22
 
0.4%
Other values (213) 4612
92.2%

Most occurring characters

ValueCountFrequency (%)
- 10000
20.0%
1 9313
18.6%
9 7201
14.4%
0 6929
13.9%
8 3727
 
7.5%
7 3678
 
7.4%
2 3485
 
7.0%
6 1604
 
3.2%
3 1522
 
3.0%
5 1371
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40000
80.0%
Dash Punctuation 10000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9313
23.3%
9 7201
18.0%
0 6929
17.3%
8 3727
9.3%
7 3678
 
9.2%
2 3485
 
8.7%
6 1604
 
4.0%
3 1522
 
3.8%
5 1371
 
3.4%
4 1170
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 10000
20.0%
1 9313
18.6%
9 7201
14.4%
0 6929
13.9%
8 3727
 
7.5%
7 3678
 
7.4%
2 3485
 
7.0%
6 1604
 
3.2%
3 1522
 
3.0%
5 1371
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 10000
20.0%
1 9313
18.6%
9 7201
14.4%
0 6929
13.9%
8 3727
 
7.5%
7 3678
 
7.4%
2 3485
 
7.0%
6 1604
 
3.2%
3 1522
 
3.0%
5 1371
 
2.7%

Age
Real number (ℝ)

Distinct216
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.19181
Minimum26.82
Maximum67.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:33.097769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum26.82
5-th percentile29.34
Q135.59
median42.81
Q347.94
95-th percentile55.72
Maximum67.58
Range40.76
Interquartile range (IQR)12.35

Descriptive statistics

Standard deviation8.4674129
Coefficient of variation (CV)0.20068854
Kurtosis-0.55881359
Mean42.19181
Median Absolute Deviation (MAD)6.43
Skewness0.16018344
Sum210959.05
Variance71.69708
MonotonicityNot monotonic
2023-06-28T10:38:33.192050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.84 64
 
1.3%
46.87 64
 
1.3%
50.27 44
 
0.9%
30.4 44
 
0.9%
27.09 44
 
0.9%
34.11 44
 
0.9%
41.06 44
 
0.9%
44.75 43
 
0.9%
34.88 43
 
0.9%
45.13 43
 
0.9%
Other values (206) 4523
90.5%
ValueCountFrequency (%)
26.82 22
0.4%
26.85 21
0.4%
27.07 22
0.4%
27.09 44
0.9%
27.84 21
0.4%
28.19 22
0.4%
28.4 21
0.4%
28.42 22
0.4%
28.83 21
0.4%
28.84 22
0.4%
ValueCountFrequency (%)
67.58 21
0.4%
62.43 22
0.4%
61.86 22
0.4%
61.31 22
0.4%
60.13 22
0.4%
59.7 21
0.4%
58.89 22
0.4%
58.7 22
0.4%
57.91 21
0.4%
57.34 21
0.4%

Emp Status
Boolean

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
5000 
ValueCountFrequency (%)
True 5000
100.0%
2023-06-28T10:38:33.286657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Emp Type
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Fulltime
5000 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters40000
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFulltime
2nd rowFulltime
3rd rowFulltime
4th rowFulltime
5th rowFulltime

Common Values

ValueCountFrequency (%)
Fulltime 5000
100.0%

Length

2023-06-28T10:38:33.349447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T10:38:33.427578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fulltime 5000
100.0%

Most occurring characters

ValueCountFrequency (%)
l 10000
25.0%
F 5000
12.5%
u 5000
12.5%
t 5000
12.5%
i 5000
12.5%
m 5000
12.5%
e 5000
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35000
87.5%
Uppercase Letter 5000
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 10000
28.6%
u 5000
14.3%
t 5000
14.3%
i 5000
14.3%
m 5000
14.3%
e 5000
14.3%
Uppercase Letter
ValueCountFrequency (%)
F 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 10000
25.0%
F 5000
12.5%
u 5000
12.5%
t 5000
12.5%
i 5000
12.5%
m 5000
12.5%
e 5000
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 10000
25.0%
F 5000
12.5%
u 5000
12.5%
t 5000
12.5%
i 5000
12.5%
m 5000
12.5%
e 5000
12.5%

Unpaid Leave
Real number (ℝ)

Distinct21
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.0326
Minimum0
Maximum20
Zeros236
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:33.474843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0208959
Coefficient of variation (CV)0.60013316
Kurtosis-1.2022859
Mean10.0326
Median Absolute Deviation (MAD)5
Skewness-0.019615914
Sum50163
Variance36.251187
MonotonicityNot monotonic
2023-06-28T10:38:33.553367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
16 255
 
5.1%
12 255
 
5.1%
17 250
 
5.0%
15 248
 
5.0%
4 244
 
4.9%
13 241
 
4.8%
8 240
 
4.8%
14 240
 
4.8%
5 239
 
4.8%
1 237
 
4.7%
Other values (11) 2551
51.0%
ValueCountFrequency (%)
0 236
4.7%
1 237
4.7%
2 223
4.5%
3 235
4.7%
4 244
4.9%
5 239
4.8%
6 236
4.7%
7 237
4.7%
8 240
4.8%
9 237
4.7%
ValueCountFrequency (%)
20 221
4.4%
19 233
4.7%
18 233
4.7%
17 250
5.0%
16 255
5.1%
15 248
5.0%
14 240
4.8%
13 241
4.8%
12 255
5.1%
11 232
4.6%

Required Hours
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
1920
5000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters20000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1920
2nd row1920
3rd row1920
4th row1920
5th row1920

Common Values

ValueCountFrequency (%)
1920 5000
100.0%

Length

2023-06-28T10:38:33.647414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T10:38:33.710352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1920 5000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5000
25.0%
9 5000
25.0%
2 5000
25.0%
0 5000
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5000
25.0%
9 5000
25.0%
2 5000
25.0%
0 5000
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5000
25.0%
9 5000
25.0%
2 5000
25.0%
0 5000
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5000
25.0%
9 5000
25.0%
2 5000
25.0%
0 5000
25.0%

Actual Hours
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
1920
5000 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters20000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1920
2nd row1920
3rd row1920
4th row1920
5th row1920

Common Values

ValueCountFrequency (%)
1920 5000
100.0%

Length

2023-06-28T10:38:33.773506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T10:38:33.852005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1920 5000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 5000
25.0%
9 5000
25.0%
2 5000
25.0%
0 5000
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5000
25.0%
9 5000
25.0%
2 5000
25.0%
0 5000
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5000
25.0%
9 5000
25.0%
2 5000
25.0%
0 5000
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5000
25.0%
9 5000
25.0%
2 5000
25.0%
0 5000
25.0%

Pre-increment salary
Real number (ℝ)

Distinct77
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22360.028
Minimum3826
Maximum323265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:33.927211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3826
5-th percentile4305
Q15101
median8768
Q319129
95-th percentile86077
Maximum323265
Range319439
Interquartile range (IQR)14028

Descriptive statistics

Standard deviation38112.315
Coefficient of variation (CV)1.7044843
Kurtosis23.104378
Mean22360.028
Median Absolute Deviation (MAD)3985
Skewness4.306244
Sum1.1180014 × 108
Variance1.4525486 × 109
MonotonicityNot monotonic
2023-06-28T10:38:34.009495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4783 596
 
11.9%
9564 284
 
5.7%
5101 242
 
4.8%
7174 190
 
3.8%
4145 176
 
3.5%
4305 171
 
3.4%
8609 169
 
3.4%
15941 149
 
3.0%
8768 132
 
2.6%
7970 109
 
2.2%
Other values (67) 2782
55.6%
ValueCountFrequency (%)
3826 44
 
0.9%
4145 176
 
3.5%
4305 171
 
3.4%
4559 44
 
0.9%
4783 596
11.9%
5022 43
 
0.9%
5101 242
4.8%
5261 43
 
0.9%
5436 44
 
0.9%
5739 66
 
1.3%
ValueCountFrequency (%)
323265 21
0.4%
223161 22
0.4%
207221 22
0.4%
191282 21
0.4%
176403 22
0.4%
143780 22
0.4%
114769 21
0.4%
111581 22
0.4%
106799 22
0.4%
102017 22
0.4%

Test
Categorical

HIGH CORRELATION  UNIFORM 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
BU2
834 
BU3
834 
BU4
833 
BU5
833 
BU6
833 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15000
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBU2
2nd rowBU3
3rd rowBU4
4th rowBU5
5th rowBU6

Common Values

ValueCountFrequency (%)
BU2 834
16.7%
BU3 834
16.7%
BU4 833
16.7%
BU5 833
16.7%
BU6 833
16.7%
BU1 833
16.7%

Length

2023-06-28T10:38:34.103765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T10:38:34.182525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bu2 834
16.7%
bu3 834
16.7%
bu4 833
16.7%
bu5 833
16.7%
bu6 833
16.7%
bu1 833
16.7%

Most occurring characters

ValueCountFrequency (%)
B 5000
33.3%
U 5000
33.3%
2 834
 
5.6%
3 834
 
5.6%
4 833
 
5.6%
5 833
 
5.6%
6 833
 
5.6%
1 833
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
66.7%
Decimal Number 5000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 834
16.7%
3 834
16.7%
4 833
16.7%
5 833
16.7%
6 833
16.7%
1 833
16.7%
Uppercase Letter
ValueCountFrequency (%)
B 5000
50.0%
U 5000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
66.7%
Common 5000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 834
16.7%
3 834
16.7%
4 833
16.7%
5 833
16.7%
6 833
16.7%
1 833
16.7%
Latin
ValueCountFrequency (%)
B 5000
50.0%
U 5000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 5000
33.3%
U 5000
33.3%
2 834
 
5.6%
3 834
 
5.6%
4 833
 
5.6%
5 833
 
5.6%
6 833
 
5.6%
1 833
 
5.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
3750 
True
1250 
ValueCountFrequency (%)
False 3750
75.0%
True 1250
 
25.0%
2023-06-28T10:38:34.269466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Test2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Non-HiPO
2679 
HiPO
2321 

Length

Max length8
Median length8
Mean length6.1432
Min length4

Characters and Unicode

Total characters30716
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHiPO
2nd rowHiPO
3rd rowNon-HiPO
4th rowHiPO
5th rowHiPO

Common Values

ValueCountFrequency (%)
Non-HiPO 2679
53.6%
HiPO 2321
46.4%

Length

2023-06-28T10:38:34.352686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T10:38:34.437888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
non-hipo 2679
53.6%
hipo 2321
46.4%

Most occurring characters

ValueCountFrequency (%)
H 5000
16.3%
i 5000
16.3%
P 5000
16.3%
O 5000
16.3%
N 2679
8.7%
o 2679
8.7%
n 2679
8.7%
- 2679
8.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17679
57.6%
Lowercase Letter 10358
33.7%
Dash Punctuation 2679
 
8.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 5000
28.3%
P 5000
28.3%
O 5000
28.3%
N 2679
15.2%
Lowercase Letter
ValueCountFrequency (%)
i 5000
48.3%
o 2679
25.9%
n 2679
25.9%
Dash Punctuation
ValueCountFrequency (%)
- 2679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28037
91.3%
Common 2679
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 5000
17.8%
i 5000
17.8%
P 5000
17.8%
O 5000
17.8%
N 2679
9.6%
o 2679
9.6%
n 2679
9.6%
Common
ValueCountFrequency (%)
- 2679
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30716
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 5000
16.3%
i 5000
16.3%
P 5000
16.3%
O 5000
16.3%
N 2679
8.7%
o 2679
8.7%
n 2679
8.7%
- 2679
8.7%

Retention
Real number (ℝ)

Distinct4579
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15546.519
Minimum1911
Maximum47441
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:34.514554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1911
5-th percentile3349.95
Q18797
median15479
Q322296.75
95-th percentile27639.35
Maximum47441
Range45530
Interquartile range (IQR)13499.75

Descriptive statistics

Standard deviation7819.6667
Coefficient of variation (CV)0.50298506
Kurtosis-1.0946384
Mean15546.519
Median Absolute Deviation (MAD)6742.5
Skewness0.035988042
Sum77732595
Variance61147188
MonotonicityNot monotonic
2023-06-28T10:38:34.609748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11913 4
 
0.1%
21771 3
 
0.1%
18358 3
 
0.1%
8745 3
 
0.1%
5451 3
 
0.1%
19776 3
 
0.1%
22896 3
 
0.1%
23554 3
 
0.1%
13862 3
 
0.1%
6123 3
 
0.1%
Other values (4569) 4969
99.4%
ValueCountFrequency (%)
1911 1
< 0.1%
1916 1
< 0.1%
1918 1
< 0.1%
1919 1
< 0.1%
1921 1
< 0.1%
1932 1
< 0.1%
1939 1
< 0.1%
1948 1
< 0.1%
1950 1
< 0.1%
1956 1
< 0.1%
ValueCountFrequency (%)
47441 1
< 0.1%
40494 1
< 0.1%
39632 1
< 0.1%
38451 1
< 0.1%
38445 1
< 0.1%
37562 1
< 0.1%
37478 1
< 0.1%
34860 1
< 0.1%
28999 1
< 0.1%
28998 1
< 0.1%

PLA
Real number (ℝ)

Distinct4555
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15485.848
Minimum1909
Maximum95853
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:34.706075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1909
5-th percentile3295.65
Q18667.5
median15231
Q322173.25
95-th percentile27610.5
Maximum95853
Range93944
Interquartile range (IQR)13505.75

Descriptive statistics

Standard deviation8248.2591
Coefficient of variation (CV)0.53263206
Kurtosis6.0678869
Mean15485.848
Median Absolute Deviation (MAD)6758
Skewness0.87900139
Sum77429239
Variance68033778
MonotonicityNot monotonic
2023-06-28T10:38:34.798289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25121 4
 
0.1%
12889 4
 
0.1%
24610 3
 
0.1%
10682 3
 
0.1%
9329 3
 
0.1%
23832 3
 
0.1%
15139 3
 
0.1%
2042 3
 
0.1%
24964 3
 
0.1%
11803 3
 
0.1%
Other values (4545) 4968
99.4%
ValueCountFrequency (%)
1909 1
< 0.1%
1911 2
< 0.1%
1917 1
< 0.1%
1919 1
< 0.1%
1922 1
< 0.1%
1932 1
< 0.1%
1942 1
< 0.1%
1955 1
< 0.1%
1968 1
< 0.1%
1973 1
< 0.1%
ValueCountFrequency (%)
95853 1
< 0.1%
91646 1
< 0.1%
87288 1
< 0.1%
86652 1
< 0.1%
83298 1
< 0.1%
75103 1
< 0.1%
73044 1
< 0.1%
54064 1
< 0.1%
49926 1
< 0.1%
43553 1
< 0.1%

PFactor
Real number (ℝ)

Distinct159
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.758
Minimum0
Maximum158
Zeros40
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:34.987604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q138
median79
Q3118
95-th percentile150
Maximum158
Range158
Interquartile range (IQR)80

Descriptive statistics

Standard deviation45.960721
Coefficient of variation (CV)0.58356893
Kurtosis-1.21184
Mean78.758
Median Absolute Deviation (MAD)40
Skewness-0.0080823705
Sum393790
Variance2112.3879
MonotonicityNot monotonic
2023-06-28T10:38:35.087221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148 45
 
0.9%
100 41
 
0.8%
150 41
 
0.8%
95 41
 
0.8%
20 41
 
0.8%
0 40
 
0.8%
29 40
 
0.8%
13 40
 
0.8%
30 40
 
0.8%
15 40
 
0.8%
Other values (149) 4591
91.8%
ValueCountFrequency (%)
0 40
0.8%
1 33
0.7%
2 25
0.5%
3 25
0.5%
4 36
0.7%
5 38
0.8%
6 34
0.7%
7 33
0.7%
8 33
0.7%
9 30
0.6%
ValueCountFrequency (%)
158 27
0.5%
157 23
0.5%
156 31
0.6%
155 22
0.4%
154 35
0.7%
153 30
0.6%
152 33
0.7%
151 27
0.5%
150 41
0.8%
149 36
0.7%

Equity
Real number (ℝ)

Distinct4849
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59791.786
Minimum20007
Maximum99992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:35.198789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20007
5-th percentile24051.4
Q140109.5
median59869.5
Q379493
95-th percentile96119
Maximum99992
Range79985
Interquartile range (IQR)39383.5

Descriptive statistics

Standard deviation23020.259
Coefficient of variation (CV)0.38500705
Kurtosis-1.1955878
Mean59791.786
Median Absolute Deviation (MAD)19665.5
Skewness0.0099403875
Sum2.9895893 × 108
Variance5.2993232 × 108
MonotonicityNot monotonic
2023-06-28T10:38:35.300022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53455 3
 
0.1%
52479 3
 
0.1%
41840 2
 
< 0.1%
76539 2
 
< 0.1%
94765 2
 
< 0.1%
37207 2
 
< 0.1%
33031 2
 
< 0.1%
60316 2
 
< 0.1%
20933 2
 
< 0.1%
41941 2
 
< 0.1%
Other values (4839) 4978
99.6%
ValueCountFrequency (%)
20007 1
< 0.1%
20023 1
< 0.1%
20024 1
< 0.1%
20033 1
< 0.1%
20042 1
< 0.1%
20045 1
< 0.1%
20064 1
< 0.1%
20084 1
< 0.1%
20109 1
< 0.1%
20158 1
< 0.1%
ValueCountFrequency (%)
99992 1
< 0.1%
99986 1
< 0.1%
99977 1
< 0.1%
99972 1
< 0.1%
99971 1
< 0.1%
99957 1
< 0.1%
99943 1
< 0.1%
99924 1
< 0.1%
99923 1
< 0.1%
99920 1
< 0.1%

Health
Real number (ℝ)

Distinct4844
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59454.576
Minimum20001
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:35.407942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20001
5-th percentile24034.95
Q139726.25
median59417
Q379209.5
95-th percentile95315.2
Maximum100000
Range79999
Interquartile range (IQR)39483.25

Descriptive statistics

Standard deviation22806.915
Coefficient of variation (CV)0.38360235
Kurtosis-1.1981451
Mean59454.576
Median Absolute Deviation (MAD)19750
Skewness-0.0016517779
Sum2.9727288 × 108
Variance5.2015537 × 108
MonotonicityNot monotonic
2023-06-28T10:38:35.509221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53935 3
 
0.1%
52268 3
 
0.1%
23645 3
 
0.1%
91101 3
 
0.1%
70571 3
 
0.1%
31580 3
 
0.1%
60937 2
 
< 0.1%
93288 2
 
< 0.1%
21120 2
 
< 0.1%
75397 2
 
< 0.1%
Other values (4834) 4974
99.5%
ValueCountFrequency (%)
20001 1
< 0.1%
20077 1
< 0.1%
20081 1
< 0.1%
20085 1
< 0.1%
20110 1
< 0.1%
20133 1
< 0.1%
20147 2
< 0.1%
20162 1
< 0.1%
20170 1
< 0.1%
20171 2
< 0.1%
ValueCountFrequency (%)
100000 1
< 0.1%
99970 1
< 0.1%
99939 1
< 0.1%
99935 1
< 0.1%
99919 1
< 0.1%
99913 1
< 0.1%
99911 1
< 0.1%
99905 1
< 0.1%
99879 1
< 0.1%
99864 1
< 0.1%

Basic
Real number (ℝ)

Distinct77
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14906.576
Minimum2551
Maximum215510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:35.622734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2551
5-th percentile2870
Q13401
median5845
Q312752
95-th percentile57385
Maximum215510
Range212959
Interquartile range (IQR)9351

Descriptive statistics

Standard deviation25408.249
Coefficient of variation (CV)1.7044994
Kurtosis23.104318
Mean14906.576
Median Absolute Deviation (MAD)2657
Skewness4.3062375
Sum74532878
Variance6.4557911 × 108
MonotonicityNot monotonic
2023-06-28T10:38:35.724257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3188 596
 
11.9%
6376 284
 
5.7%
3401 242
 
4.8%
4783 190
 
3.8%
2763 176
 
3.5%
2870 171
 
3.4%
5739 169
 
3.4%
10627 149
 
3.0%
5845 132
 
2.6%
5314 109
 
2.2%
Other values (67) 2782
55.6%
ValueCountFrequency (%)
2551 44
 
0.9%
2763 176
 
3.5%
2870 171
 
3.4%
3040 44
 
0.9%
3188 596
11.9%
3348 43
 
0.9%
3401 242
4.8%
3507 43
 
0.9%
3624 44
 
0.9%
3826 66
 
1.3%
ValueCountFrequency (%)
215510 21
0.4%
148774 22
0.4%
138148 22
0.4%
127521 21
0.4%
117602 22
0.4%
95853 22
0.4%
76513 21
0.4%
74387 22
0.4%
71199 22
0.4%
68011 22
0.4%

Housing
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct77
Distinct (%)10.6%
Missing4273
Missing (%)85.5%
Infinite0
Infinite (%)0.0%
Mean10451.832
Minimum1700
Maximum143673
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-06-28T10:38:35.817385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1700
5-th percentile1913
Q12267
median3968
Q39918
95-th percentile42507
Maximum143673
Range141973
Interquartile range (IQR)7651

Descriptive statistics

Standard deviation17654.769
Coefficient of variation (CV)1.6891554
Kurtosis20.163647
Mean10451.832
Median Absolute Deviation (MAD)1842
Skewness4.0563752
Sum7598482
Variance3.1169086 × 108
MonotonicityNot monotonic
2023-06-28T10:38:35.911667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2126 84
 
1.7%
4251 39
 
0.8%
2267 34
 
0.7%
3188 28
 
0.6%
1842 24
 
0.5%
1913 24
 
0.5%
3826 24
 
0.5%
7085 22
 
0.4%
14169 18
 
0.4%
3897 18
 
0.4%
Other values (67) 412
 
8.2%
(Missing) 4273
85.5%
ValueCountFrequency (%)
1700 7
 
0.1%
1842 24
 
0.5%
1913 24
 
0.5%
2026 6
 
0.1%
2126 84
1.7%
2232 7
 
0.1%
2267 34
0.7%
2338 6
 
0.1%
2416 6
 
0.1%
2551 9
 
0.2%
ValueCountFrequency (%)
143673 3
0.1%
99183 4
0.1%
92098 4
0.1%
85014 3
0.1%
78401 4
0.1%
63902 4
0.1%
51008 3
0.1%
49592 3
0.1%
47466 3
0.1%
45341 4
0.1%

Interactions

2023-06-28T10:38:28.952748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:07.731436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.168717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.550299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:11.942584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:13.438454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.736303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:16.106410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:17.608946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.035419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:20.513958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.836523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.167969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:24.672232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:26.122728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.542070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:29.035141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:07.906202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.234171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.636086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:12.120119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:13.505632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.819206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:16.192196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:17.687107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.118079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:20.584168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.907476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.254473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:24.749119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:26.200762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.625228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:29.127674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.000795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.332327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.726326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:12.202545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:13.584474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.904983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:16.281603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:17.786886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.199099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:20.669725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.983765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.338407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:24.849646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:26.299216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.703914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:29.219476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.093450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.416778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.800089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:12.302933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:13.672713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.000749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:16.475387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:17.876345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.283405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:20.750434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:22.086475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.418864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:24.938915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:26.385000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.784995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:29.300145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.170201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.501135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.897688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:12.386923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:13.751430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.084115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:16.569809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:17.967821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.373154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:20.836983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:22.166469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.519219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:25.040724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:26.485047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.867397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:29.383640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.256937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.587124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.975183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:12.469267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:13.836405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.165838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:16.643735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:18.034358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.458363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:20.918395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:22.235035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.604378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:25.120825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:26.569479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.936935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:29.472471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.332109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.668386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:11.069785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:12.555416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:13.903519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.243830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:16.734552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:18.118769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.535955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.003548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:22.323219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.684494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:25.221073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:26.654202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:28.019269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:29.568422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.419898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.750412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:11.149446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:12.638965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.003756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.344284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:16.820482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:18.219731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.618968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.087565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:22.419131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.768277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:25.319640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:26.741472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:28.109146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:29.650187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.508126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.850416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:11.251218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:12.737399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.086195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.418819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:16.914068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:18.310802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.718305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.171617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:22.500373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.881949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:25.419821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:26.837271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:28.190721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:29.735918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.588303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.952800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:11.338055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:12.822667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.169104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.505774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:16.998341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:18.401609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.812933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.260108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:22.588031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.972970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:25.502242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:26.924272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:28.271790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:29.829253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.653449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.032379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:11.417880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:12.905349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.235866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.587000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:17.069637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:18.486094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.889089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.319395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:22.668696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:24.053927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:25.592532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.004837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:28.354356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:29.903910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.739844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.099370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:11.501003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:12.986070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.317029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.670179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:17.154576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:18.572348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:19.977445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.404427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:22.738760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:24.119939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:25.667896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.090956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:28.426692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:30.003402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.816255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.204458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:11.587043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:13.066754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.404406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.764689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:17.250857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:18.668987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:20.067148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.486400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:22.836463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:24.224625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:25.771844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.183733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:28.507711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:30.096048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:08.920982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.296534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:11.668302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:13.169421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.484198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.858771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:17.337646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:18.769598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:20.153035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.573946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:22.919004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:24.300438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:25.854020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.268485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:28.602277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:30.190636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.009801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.383671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:11.771260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:13.267155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.584859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:15.934336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:17.438545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:18.855934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:20.251135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.669244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.003667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:24.407366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:25.954119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.370214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:28.683935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:30.269808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:09.067849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:10.467518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:11.849678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:13.334168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:14.652588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:16.016793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:17.520727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:18.934251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:20.416210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:21.735701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:23.086481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:24.485581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:26.032996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:27.449709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T10:38:28.870499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-28T10:38:36.001879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Employee IDLevelTenureSalarySalary MinSalary MaxAgeUnpaid LeavePre-increment salaryRetentionPLAPFactorEquityHealthBasicHousingTitleDepartmentGenderPerformance RatingTestPromotion EligibilityTest2
Employee ID1.000-0.0070.007-0.002-0.007-0.0070.010-0.009-0.002-0.003-0.023-0.0110.0270.023-0.0020.0280.0000.0620.0190.0000.0000.0000.122
Level-0.0071.0000.0370.8211.0001.000-0.0470.0060.821-0.009-0.0050.0350.0120.0140.8210.8350.1980.3050.0970.0000.1040.0000.000
Tenure0.0070.0371.0000.0230.0370.0370.393-0.0110.0230.002-0.0110.002-0.0010.0080.0230.0390.2020.4100.2230.0000.0950.0000.000
Salary-0.0020.8210.0231.0000.8210.821-0.050-0.0041.0000.002-0.0040.034-0.0070.0171.0001.0000.1960.1990.1570.0000.0800.0000.000
Salary Min-0.0071.0000.0370.8211.0001.000-0.0470.0060.821-0.009-0.0050.0350.0120.0140.8210.8350.1650.2020.0810.0110.0950.0000.000
Salary Max-0.0071.0000.0370.8211.0001.000-0.0470.0060.821-0.009-0.0050.0350.0120.0140.8210.8350.1650.2020.0810.0110.0950.0000.000
Age0.010-0.0470.393-0.050-0.047-0.0471.000-0.027-0.0500.0010.003-0.009-0.0080.032-0.050-0.0600.2060.2430.1660.0000.0970.0000.000
Unpaid Leave-0.0090.006-0.011-0.0040.0060.006-0.0271.000-0.004-0.017-0.0060.008-0.023-0.003-0.004-0.0240.0000.0330.0450.0000.0150.0000.000
Pre-increment salary-0.0020.8210.0231.0000.8210.821-0.050-0.0041.0000.002-0.0040.034-0.0070.0171.0001.0000.2020.2030.1570.0000.0820.0000.000
Retention-0.003-0.0090.0020.002-0.009-0.0090.001-0.0170.0021.0000.0090.0080.015-0.0050.0020.0540.0000.0620.0420.0000.0000.0150.000
PLA-0.023-0.005-0.011-0.004-0.005-0.0050.003-0.006-0.0040.0091.000-0.0020.0240.007-0.0040.0400.0000.0380.0320.0000.0000.0210.013
PFactor-0.0110.0350.0020.0340.0350.035-0.0090.0080.0340.008-0.0021.0000.012-0.0030.0340.0850.0100.0000.0000.0080.0000.0070.000
Equity0.0270.012-0.001-0.0070.0120.012-0.008-0.023-0.0070.0150.0240.0121.000-0.014-0.007-0.0020.0320.0000.0000.0200.0000.0040.000
Health0.0230.0140.0080.0170.0140.0140.032-0.0030.017-0.0050.007-0.003-0.0141.0000.017-0.0120.0000.0250.0000.0000.0000.0000.000
Basic-0.0020.8210.0231.0000.8210.821-0.050-0.0041.0000.002-0.0040.034-0.0070.0171.0001.0000.2020.2030.1570.0000.0820.0000.000
Housing0.0280.8350.0391.0000.8350.835-0.060-0.0241.0000.0540.0400.085-0.002-0.0121.0001.0000.1940.1870.1290.0330.0440.0000.000
Title0.0000.1980.2020.1960.1650.1650.2060.0000.2020.0000.0000.0100.0320.0000.2020.1941.0000.1130.1610.0000.2950.0000.000
Department0.0620.3050.4100.1990.2020.2020.2430.0330.2030.0620.0380.0000.0000.0250.2030.1870.1131.0000.1160.0170.0550.0000.000
Gender0.0190.0970.2230.1570.0810.0810.1660.0450.1570.0420.0320.0000.0000.0000.1570.1290.1610.1161.0000.0000.0780.0000.000
Performance Rating0.0000.0000.0000.0000.0110.0110.0000.0000.0000.0000.0000.0080.0200.0000.0000.0330.0000.0170.0001.0000.0000.0000.010
Test0.0000.1040.0950.0800.0950.0950.0970.0150.0820.0000.0000.0000.0000.0000.0820.0440.2950.0550.0780.0001.0000.5770.308
Promotion Eligibility0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0210.0070.0040.0000.0000.0000.0000.0000.0000.0000.5771.0000.178
Test20.1220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0100.3080.1781.000

Missing values

2023-06-28T10:38:30.443080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-28T10:38:30.756806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Employee IDNameTitleLevelDepartmentGenderHire DateTenureSupervisor IDPerformance RatingSalarySalary MinSalary MaxDatte of BirthAgeEmp StatusEmp TypeUnpaid LeaveRequired HoursActual HoursPre-increment salaryTestPromotion EligibilityTest2RetentionPLAPFactorEquityHealthBasicHousing
02140092Anfas Last NameManger5ManagementFemale04-01-20158.432140092446050433505865011-01-198736.41YesFulltime71920192041445BU2NoHiPO27475872881675145429342763018420.0
12150027Lennard Last NameOfficer9Shared ServicesMale22-03-20158.222140092123024619635026565030-03-198043.19YesFulltime819201920207221BU3NoHiPO272909585366967636367313814892098.0
22160393Edgardo Last NameExecutive4Shared ServicesMale01-06-20167.032140092235423280503795008-06-199132.01YesFulltime31920192031881BU4NoNon-HiPO23331866527791727393762125414169.0
32160353Channa Last NameAssistant Manager4ManagementFemale15-05-20167.072140092121254280503795021-05-199231.05YesFulltime101920192019129BU5YesHiPO3747849926779462751561127528502.0
42080025Linda Last NameSenior Manager4ManagementFemale01-09-200814.782140092435423280503795009-09-197844.75YesFulltime51920192031881BU6NoHiPO3756229999632141840462125414169.0
52150066Mairaj Last NameFinance Manager6Shared ServicesMale17-05-20158.06214009211597558500011500023-05-199132.05YesFulltime2019201920143780BU1NoHiPO239527510313163945406949585363902.0
62150158Ajay Last NameOfficer6Shared ServicesFemale29-11-20157.5321400925708458500011500006-12-198438.51YesFulltime41920192063761BU2NoNon-HiPO40494916465422421833594250728338.0
72160183Subramaniya Last NameExecutive6Shared ServicesMale27-03-20167.20214009231062688500011500002-04-199528.19YesFulltime111920192095641BU3YesHiPO396323105310434045663016376142507.0
82160252Rahul Last NameOfficer5Shared ServicesMale24-04-20167.132140092553134433505865001-05-198934.11YesFulltime101920192047821BU4NoHiPO474418329812444600627693188021254.0
92160270Muhammed Last NameExecutive4ManagementMale04-05-20167.102140092533652280503795011-05-199132.08YesFulltime41920192030287BU5NoHiPO384512787215379292382892019113461.0
Employee IDNameTitleLevelDepartmentGenderHire DateTenureSupervisor IDPerformance RatingSalarySalary MinSalary MaxDatte of BirthAgeEmp StatusEmp TypeUnpaid LeaveRequired HoursActual HoursPre-increment salaryTestPromotion EligibilityTest2RetentionPLAPFactorEquityHealthBasicHousing
49909144823Arun Last NameExecutive1F&BMale26-06-20166.962140092453145950805006-07-197745.93YesFulltime3192019204783BU6NoNon-HiPO144342762013974490456523188NaN
49919144824Timothy Last NameOfficer1F&BMale28-06-20166.952140092147835950805004-07-199329.93YesFulltime16192019204305BU1YesNon-HiPO24868248465335549769582870NaN
49929144825Ken Last NameExecutive4Shared ServicesFemale28-06-20166.952140092124796280503795009-07-197349.92YesFulltime181920192022316BU2NoNon-HiPO32592236974737269739914878NaN
49939144826Tentus Last NameOfficer1F&BMale02-07-20166.942140092353145950805014-07-197151.91YesFulltime10192019204783BU3NoNon-HiPO15462229544927862883863188NaN
49949144827Flowrens Last NameExecutive1F&BMale02-07-20166.942140092453145950805011-07-198141.91YesFulltime14192019204783BU4NoNon-HiPO152252303514826586523563188NaN
49959144828Vishnu Last NameOfficer3F&BFemale04-07-20166.93214009259742161502185015-07-197349.90YesFulltime4192019208768BU5YesNon-HiPO6385113927327086587505845NaN
49969144829Mireille Last NameExecutive5F&BFemale09-07-20166.922140092253134433505865020-07-197547.89YesFulltime01920192047821BU6NoNon-HiPO79174057150616463434431880NaN
49979144830Vidurangani Last NameManger2F&BMale09-07-20166.9221400922956593501265018-07-198240.89YesFulltime6192019208609BU1NoNon-HiPO1501757393573384637215739NaN
49989144831George Last NameOfficer1F&BFemale16-07-20166.902140092247835950805026-07-197646.87YesFulltime10192019204305BU2NoNon-HiPO12182230614490642553222870NaN
49999144832Peter Last NameExecutive1F&BMale16-07-20166.902140092347835950805023-07-198834.88YesFulltime4192019204305BU3YesNon-HiPO5406181737455963309482870NaN